Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations2270250
Missing cells10144783
Missing cells (%)27.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory649.1 MiB
Average record size in memory299.8 B

Variable types

Text1
DateTime1
Numeric13
Categorical1

Alerts

AQI is highly overall correlated with PM10 and 1 other fieldsHigh correlation
Benzene is highly overall correlated with Toluene and 1 other fieldsHigh correlation
NO is highly overall correlated with NOxHigh correlation
NO2 is highly overall correlated with NOxHigh correlation
NOx is highly overall correlated with NO and 1 other fieldsHigh correlation
PM10 is highly overall correlated with AQI and 1 other fieldsHigh correlation
PM2.5 is highly overall correlated with AQI and 1 other fieldsHigh correlation
Toluene is highly overall correlated with Benzene and 1 other fieldsHigh correlation
Xylene is highly overall correlated with Benzene and 1 other fieldsHigh correlation
PM2.5 has 586093 (25.8%) missing values Missing
PM10 has 877210 (38.6%) missing values Missing
NO has 498538 (22.0%) missing values Missing
NO2 has 475550 (20.9%) missing values Missing
NOx has 429916 (18.9%) missing values Missing
NH3 has 1041931 (45.9%) missing values Missing
CO has 445276 (19.6%) missing values Missing
SO2 has 684419 (30.1%) missing values Missing
O3 has 624138 (27.5%) missing values Missing
Benzene has 751495 (33.1%) missing values Missing
Toluene has 929412 (40.9%) missing values Missing
Xylene has 1781769 (78.5%) missing values Missing
AQI has 509518 (22.4%) missing values Missing
AQI_Bucket has 509518 (22.4%) missing values Missing
CO is highly skewed (γ1 = 32.05639945) Skewed
Benzene is highly skewed (γ1 = 29.2881114) Skewed
NOx has 116602 (5.1%) zeros Zeros
CO has 153506 (6.8%) zeros Zeros
Benzene has 333688 (14.7%) zeros Zeros
Toluene has 262296 (11.6%) zeros Zeros
Xylene has 207980 (9.2%) zeros Zeros

Reproduction

Analysis started2024-12-29 05:26:48.190197
Analysis finished2024-12-29 05:29:31.012239
Duration2 minutes and 42.82 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Distinct96
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size134.2 MiB
2024-12-29T05:29:31.391713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters11351250
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAP001
2nd rowAP001
3rd rowAP001
4th rowAP001
5th rowAP001
ValueCountFrequency (%)
gj001 48192
 
2.1%
dl007 48192
 
2.1%
tn001 48192
 
2.1%
dl033 48192
 
2.1%
dl021 48192
 
2.1%
ka003 48192
 
2.1%
ka009 48192
 
2.1%
dl013 48192
 
2.1%
mh005 48192
 
2.1%
dl008 48192
 
2.1%
Other values (86) 1788330
78.8%
2024-12-29T05:29:32.141591image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3655390
32.2%
L 1132467
 
10.0%
D 1132017
 
10.0%
1 949664
 
8.4%
3 490259
 
4.3%
2 468339
 
4.1%
A 357403
 
3.1%
K 325241
 
2.9%
5 286273
 
2.5%
T 275802
 
2.4%
Other values (17) 2278395
20.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11351250
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 3655390
32.2%
L 1132467
 
10.0%
D 1132017
 
10.0%
1 949664
 
8.4%
3 490259
 
4.3%
2 468339
 
4.1%
A 357403
 
3.1%
K 325241
 
2.9%
5 286273
 
2.5%
T 275802
 
2.4%
Other values (17) 2278395
20.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11351250
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 3655390
32.2%
L 1132467
 
10.0%
D 1132017
 
10.0%
1 949664
 
8.4%
3 490259
 
4.3%
2 468339
 
4.1%
A 357403
 
3.1%
K 325241
 
2.9%
5 286273
 
2.5%
T 275802
 
2.4%
Other values (17) 2278395
20.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11351250
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 3655390
32.2%
L 1132467
 
10.0%
D 1132017
 
10.0%
1 949664
 
8.4%
3 490259
 
4.3%
2 468339
 
4.1%
A 357403
 
3.1%
K 325241
 
2.9%
5 286273
 
2.5%
T 275802
 
2.4%
Other values (17) 2278395
20.1%
Distinct48192
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size17.3 MiB
Minimum2015-01-01 01:00:00
Maximum2020-07-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2024-12-29T05:29:32.493683image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:32.858454image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

PM2.5
Real number (ℝ)

High correlation  Missing 

Distinct42154
Distinct (%)2.5%
Missing586093
Missing (%)25.8%
Infinite0
Infinite (%)0.0%
Mean81.158274
Minimum0.01
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:33.227072image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile10
Q128.5
median52.75
Q397
95-th percentile253.7
Maximum1000
Range999.99
Interquartile range (IQR)68.5

Descriptive statistics

Standard deviation90.269788
Coefficient of variation (CV)1.1122685
Kurtosis18.283875
Mean81.158274
Median Absolute Deviation (MAD)29.25
Skewness3.3631464
Sum1.3668327 × 108
Variance8148.6347
MonotonicityNot monotonic
2024-12-29T05:29:33.575501image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 4844
 
0.2%
27 4813
 
0.2%
56 4684
 
0.2%
33 4590
 
0.2%
28 4577
 
0.2%
29 4546
 
0.2%
39 4543
 
0.2%
40 4500
 
0.2%
38 4455
 
0.2%
25 4432
 
0.2%
Other values (42144) 1638173
72.2%
(Missing) 586093
 
25.8%
ValueCountFrequency (%)
0.01 31
< 0.1%
0.02 46
< 0.1%
0.03 62
< 0.1%
0.04 49
< 0.1%
0.05 42
< 0.1%
0.06 32
< 0.1%
0.07 38
< 0.1%
0.08 42
< 0.1%
0.09 36
< 0.1%
0.1 59
< 0.1%
ValueCountFrequency (%)
1000 182
< 0.1%
999.99 359
< 0.1%
999.75 2
 
< 0.1%
999.5 2
 
< 0.1%
999.25 1
 
< 0.1%
998.75 1
 
< 0.1%
998 1
 
< 0.1%
997.51 1
 
< 0.1%
997.5 1
 
< 0.1%
997.45 1
 
< 0.1%

PM10
Real number (ℝ)

High correlation  Missing 

Distinct56323
Distinct (%)4.0%
Missing877210
Missing (%)38.6%
Infinite0
Infinite (%)0.0%
Mean161.5534
Minimum0.01
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:33.905952image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile26.05
Q166.5
median118.75
Q3207.8325
95-th percentile447
Maximum1000
Range999.99
Interquartile range (IQR)141.3325

Descriptive statistics

Standard deviation141.1247
Coefficient of variation (CV)0.87354832
Kurtosis5.4217206
Mean161.5534
Median Absolute Deviation (MAD)62.5
Skewness2.0290261
Sum2.2505034 × 108
Variance19916.18
MonotonicityNot monotonic
2024-12-29T05:29:34.258467image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
94 4812
 
0.2%
56 1860
 
0.1%
69 1821
 
0.1%
59 1814
 
0.1%
53 1809
 
0.1%
80 1799
 
0.1%
64 1772
 
0.1%
86 1767
 
0.1%
50 1758
 
0.1%
72 1751
 
0.1%
Other values (56313) 1372077
60.4%
(Missing) 877210
38.6%
ValueCountFrequency (%)
0.01 24
< 0.1%
0.02 30
< 0.1%
0.03 45
< 0.1%
0.04 35
< 0.1%
0.05 36
< 0.1%
0.06 49
< 0.1%
0.07 32
< 0.1%
0.08 44
< 0.1%
0.09 37
< 0.1%
0.1 46
< 0.1%
ValueCountFrequency (%)
1000 189
< 0.1%
999.99 234
< 0.1%
999.96 1
 
< 0.1%
999.83 1
 
< 0.1%
999.75 3
 
< 0.1%
999.33 1
 
< 0.1%
999.16 1
 
< 0.1%
999 2
 
< 0.1%
998.9 1
 
< 0.1%
998.58 1
 
< 0.1%

NO
Real number (ℝ)

High correlation  Missing 

Distinct35996
Distinct (%)2.0%
Missing498538
Missing (%)22.0%
Infinite0
Infinite (%)0.0%
Mean23.6537
Minimum0.01
Maximum500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:34.582608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.84
Q12.97
median7.15
Q320.3
95-th percentile106.92
Maximum500
Range499.99
Interquartile range (IQR)17.33

Descriptive statistics

Standard deviation49.339692
Coefficient of variation (CV)2.0859186
Kurtosis26.828511
Mean23.6537
Median Absolute Deviation (MAD)5.25
Skewness4.6254692
Sum41907544
Variance2434.4052
MonotonicityNot monotonic
2024-12-29T05:29:34.945164image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.8 3803
 
0.2%
1.5 3741
 
0.2%
1 3712
 
0.2%
0.9 3685
 
0.2%
1.1 3587
 
0.2%
1.4 3526
 
0.2%
0.7 3502
 
0.2%
1.3 3451
 
0.2%
0.6 3450
 
0.2%
1.6 3450
 
0.2%
Other values (35986) 1735805
76.5%
(Missing) 498538
 
22.0%
ValueCountFrequency (%)
0.01 166
 
< 0.1%
0.02 229
 
< 0.1%
0.03 239
 
< 0.1%
0.04 185
 
< 0.1%
0.05 216
 
< 0.1%
0.06 183
 
< 0.1%
0.07 215
 
< 0.1%
0.08 243
 
< 0.1%
0.09 152
 
< 0.1%
0.1 1369
0.1%
ValueCountFrequency (%)
500 1
 
< 0.1%
499.99 2
< 0.1%
499.9 1
 
< 0.1%
499.8 3
< 0.1%
499.76 1
 
< 0.1%
499.74 1
 
< 0.1%
499.7 2
< 0.1%
499.67 1
 
< 0.1%
499.65 1
 
< 0.1%
499.63 1
 
< 0.1%

NO2
Real number (ℝ)

High correlation  Missing 

Distinct25948
Distinct (%)1.4%
Missing475550
Missing (%)20.9%
Infinite0
Infinite (%)0.0%
Mean35.856924
Minimum0.01
Maximum499.99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:35.274865image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile4.29
Q113.38
median25.35
Q346.5
95-th percentile101.4
Maximum499.99
Range499.98
Interquartile range (IQR)33.12

Descriptive statistics

Standard deviation35.296569
Coefficient of variation (CV)0.98437248
Kurtosis16.94144
Mean35.856924
Median Absolute Deviation (MAD)14.37
Skewness3.0135721
Sum64352422
Variance1245.8478
MonotonicityNot monotonic
2024-12-29T05:29:35.617814image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.2 1131
 
< 0.1%
9 1059
 
< 0.1%
15 993
 
< 0.1%
24 928
 
< 0.1%
14 924
 
< 0.1%
10 914
 
< 0.1%
14.5 908
 
< 0.1%
12 891
 
< 0.1%
11 882
 
< 0.1%
15.5 875
 
< 0.1%
Other values (25938) 1785195
78.6%
(Missing) 475550
 
20.9%
ValueCountFrequency (%)
0.01 163
 
< 0.1%
0.02 300
< 0.1%
0.03 342
< 0.1%
0.04 298
< 0.1%
0.05 246
 
< 0.1%
0.06 249
 
< 0.1%
0.07 269
< 0.1%
0.08 232
 
< 0.1%
0.09 190
 
< 0.1%
0.1 664
< 0.1%
ValueCountFrequency (%)
499.99 2
< 0.1%
499.97 1
< 0.1%
499.8 1
< 0.1%
499.51 1
< 0.1%
499.28 1
< 0.1%
498.96 1
< 0.1%
498.4 1
< 0.1%
498.3 1
< 0.1%
497.94 1
< 0.1%
497.81 1
< 0.1%

NOx
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct39904
Distinct (%)2.2%
Missing429916
Missing (%)18.9%
Infinite0
Infinite (%)0.0%
Mean41.496408
Minimum0
Maximum500
Zeros116602
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:35.953998image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.46
median23.45
Q347.12
95-th percentile146.51
Maximum500
Range500
Interquartile range (IQR)35.66

Descriptive statistics

Standard deviation56.632448
Coefficient of variation (CV)1.3647554
Kurtosis16.203575
Mean41.496408
Median Absolute Deviation (MAD)14.88
Skewness3.5120335
Sum76367251
Variance3207.2342
MonotonicityNot monotonic
2024-12-29T05:29:36.378579image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 116602
 
5.1%
5 1307
 
0.1%
4.9 1187
 
0.1%
10 1047
 
< 0.1%
9 1021
 
< 0.1%
7 1010
 
< 0.1%
11.5 971
 
< 0.1%
11 948
 
< 0.1%
12 896
 
< 0.1%
9.5 889
 
< 0.1%
Other values (39894) 1714456
75.5%
(Missing) 429916
 
18.9%
ValueCountFrequency (%)
0 116602
5.1%
0.01 79
 
< 0.1%
0.02 79
 
< 0.1%
0.03 245
 
< 0.1%
0.04 81
 
< 0.1%
0.05 184
 
< 0.1%
0.06 40
 
< 0.1%
0.07 65
 
< 0.1%
0.08 121
 
< 0.1%
0.09 52
 
< 0.1%
ValueCountFrequency (%)
500 4
< 0.1%
499.99 2
< 0.1%
499.97 1
 
< 0.1%
499.95 1
 
< 0.1%
499.94 1
 
< 0.1%
499.9 2
< 0.1%
499.87 1
 
< 0.1%
499.82 1
 
< 0.1%
499.8 2
< 0.1%
499.73 1
 
< 0.1%

NH3
Real number (ℝ)

Missing 

Distinct18388
Distinct (%)1.5%
Missing1041931
Missing (%)45.9%
Infinite0
Infinite (%)0.0%
Mean28.486834
Minimum0.01
Maximum499.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:37.027672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile3.62
Q111.46
median22.53
Q337.9
95-th percentile72.42
Maximum499.97
Range499.96
Interquartile range (IQR)26.44

Descriptive statistics

Standard deviation25.860621
Coefficient of variation (CV)0.90780959
Kurtosis30.47983
Mean28.486834
Median Absolute Deviation (MAD)12.35
Skewness3.6194471
Sum34990919
Variance668.77172
MonotonicityNot monotonic
2024-12-29T05:29:37.482378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 1403
 
0.1%
9 1193
 
0.1%
14.62 954
 
< 0.1%
7 881
 
< 0.1%
5 868
 
< 0.1%
26 844
 
< 0.1%
0.1 824
 
< 0.1%
13 801
 
< 0.1%
10.3 796
 
< 0.1%
12 793
 
< 0.1%
Other values (18378) 1218962
53.7%
(Missing) 1041931
45.9%
ValueCountFrequency (%)
0.01 115
 
< 0.1%
0.02 101
 
< 0.1%
0.03 133
 
< 0.1%
0.04 81
 
< 0.1%
0.05 146
 
< 0.1%
0.06 51
 
< 0.1%
0.07 66
 
< 0.1%
0.08 139
 
< 0.1%
0.09 36
 
< 0.1%
0.1 824
< 0.1%
ValueCountFrequency (%)
499.97 1
< 0.1%
499.56 1
< 0.1%
499.12 1
< 0.1%
498.54 1
< 0.1%
497.99 1
< 0.1%
494.11 1
< 0.1%
493.6 1
< 0.1%
493.22 1
< 0.1%
492.53 1
< 0.1%
491.95 1
< 0.1%

CO
Real number (ℝ)

Missing  Skewed  Zeros 

Distinct7192
Distinct (%)0.4%
Missing445276
Missing (%)19.6%
Infinite0
Infinite (%)0.0%
Mean1.5532694
Minimum0
Maximum498.57
Zeros153506
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:38.005804image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.42
median0.8
Q31.4
95-th percentile3.74
Maximum498.57
Range498.57
Interquartile range (IQR)0.98

Descriptive statistics

Standard deviation6.6755007
Coefficient of variation (CV)4.2977095
Kurtosis1474.0114
Mean1.5532694
Median Absolute Deviation (MAD)0.45
Skewness32.056399
Sum2834676.3
Variance44.56231
MonotonicityNot monotonic
2024-12-29T05:29:38.605592image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 153506
 
6.8%
0.7 20313
 
0.9%
0.8 19566
 
0.9%
0.6 19442
 
0.9%
0.5 19336
 
0.9%
0.4 19233
 
0.8%
0.3 15795
 
0.7%
0.55 14926
 
0.7%
0.53 14645
 
0.6%
0.72 14639
 
0.6%
Other values (7182) 1513573
66.7%
(Missing) 445276
 
19.6%
ValueCountFrequency (%)
0 153506
6.8%
0.01 3252
 
0.1%
0.02 2684
 
0.1%
0.03 2081
 
0.1%
0.04 2062
 
0.1%
0.05 2350
 
0.1%
0.06 2158
 
0.1%
0.07 2381
 
0.1%
0.08 2907
 
0.1%
0.09 2334
 
0.1%
ValueCountFrequency (%)
498.57 1
< 0.1%
494.9 1
< 0.1%
490.35 1
< 0.1%
485.73 1
< 0.1%
483.37 1
< 0.1%
476.2 1
< 0.1%
476.02 1
< 0.1%
475.4 1
< 0.1%
473.81 1
< 0.1%
470.72 1
< 0.1%

SO2
Real number (ℝ)

Missing 

Distinct15102
Distinct (%)1.0%
Missing684419
Missing (%)30.1%
Infinite0
Infinite (%)0.0%
Mean12.710465
Minimum0.01
Maximum199.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:39.169906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile1.41
Q14.46
median8.74
Q315.3
95-th percentile36.62
Maximum199.96
Range199.95
Interquartile range (IQR)10.84

Descriptive statistics

Standard deviation15.125598
Coefficient of variation (CV)1.1900114
Kurtosis33.144321
Mean12.710465
Median Absolute Deviation (MAD)4.96
Skewness4.5884278
Sum20156650
Variance228.78373
MonotonicityNot monotonic
2024-12-29T05:29:39.836864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 2987
 
0.1%
2.5 2662
 
0.1%
2 2635
 
0.1%
4 2525
 
0.1%
3.5 2403
 
0.1%
5.5 2340
 
0.1%
5 2318
 
0.1%
2.9 2214
 
0.1%
0.1 2199
 
0.1%
1.5 2182
 
0.1%
Other values (15092) 1561366
68.8%
(Missing) 684419
30.1%
ValueCountFrequency (%)
0.01 278
 
< 0.1%
0.02 350
 
< 0.1%
0.03 395
 
< 0.1%
0.04 374
 
< 0.1%
0.05 305
 
< 0.1%
0.06 294
 
< 0.1%
0.07 270
 
< 0.1%
0.08 294
 
< 0.1%
0.09 245
 
< 0.1%
0.1 2199
0.1%
ValueCountFrequency (%)
199.96 2
< 0.1%
199.93 1
< 0.1%
199.9 1
< 0.1%
199.85 1
< 0.1%
199.81 1
< 0.1%
199.77 1
< 0.1%
199.75 1
< 0.1%
199.72 1
< 0.1%
199.7 1
< 0.1%
199.65 1
< 0.1%

O3
Real number (ℝ)

Missing 

Distinct23096
Distinct (%)1.4%
Missing624138
Missing (%)27.5%
Infinite0
Infinite (%)0.0%
Mean38.661204
Minimum0.01
Maximum997
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:40.525359image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile2.92
Q110.89
median24.77
Q350.23
95-th percentile113.87
Maximum997
Range996.99
Interquartile range (IQR)39.34

Descriptive statistics

Standard deviation48.856479
Coefficient of variation (CV)1.2637082
Kurtosis55.828533
Mean38.661204
Median Absolute Deviation (MAD)16.7
Skewness5.4764468
Sum63640671
Variance2386.9555
MonotonicityNot monotonic
2024-12-29T05:29:41.197245image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 1415
 
0.1%
2 1266
 
0.1%
3 1252
 
0.1%
4 1150
 
0.1%
1 1130
 
< 0.1%
3.5 1079
 
< 0.1%
1.5 1056
 
< 0.1%
5.5 1050
 
< 0.1%
7 1047
 
< 0.1%
4.5 1037
 
< 0.1%
Other values (23086) 1634630
72.0%
(Missing) 624138
 
27.5%
ValueCountFrequency (%)
0.01 182
 
< 0.1%
0.02 193
 
< 0.1%
0.03 163
 
< 0.1%
0.04 123
 
< 0.1%
0.05 103
 
< 0.1%
0.06 118
 
< 0.1%
0.07 123
 
< 0.1%
0.08 110
 
< 0.1%
0.09 94
 
< 0.1%
0.1 621
< 0.1%
ValueCountFrequency (%)
997 1
< 0.1%
996 2
< 0.1%
992 1
< 0.1%
989 1
< 0.1%
988.17 1
< 0.1%
984.33 1
< 0.1%
982 2
< 0.1%
980 1
< 0.1%
977 1
< 0.1%
975 1
< 0.1%

Benzene
Real number (ℝ)

High correlation  Missing  Skewed  Zeros 

Distinct7409
Distinct (%)0.5%
Missing751495
Missing (%)33.1%
Infinite0
Infinite (%)0.0%
Mean2.9410816
Minimum0
Maximum498.07
Zeros333688
Zeros (%)14.7%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:41.532993image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median0.96
Q33.22
95-th percentile10.97
Maximum498.07
Range498.07
Interquartile range (IQR)3.12

Descriptive statistics

Standard deviation10.915072
Coefficient of variation (CV)3.711244
Kurtosis1105.2071
Mean2.9410816
Median Absolute Deviation (MAD)0.96
Skewness29.288111
Sum4466782.4
Variance119.13879
MonotonicityNot monotonic
2024-12-29T05:29:41.873487image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 333688
 
14.7%
0.1 43367
 
1.9%
0.2 22192
 
1.0%
0.3 15510
 
0.7%
0.4 12959
 
0.6%
0.35 9018
 
0.4%
0.45 8261
 
0.4%
0.15 8210
 
0.4%
0.23 7794
 
0.3%
0.6 7698
 
0.3%
Other values (7399) 1050058
46.3%
(Missing) 751495
33.1%
ValueCountFrequency (%)
0 333688
14.7%
0.01 4030
 
0.2%
0.02 2980
 
0.1%
0.03 6207
 
0.3%
0.04 3787
 
0.2%
0.05 5918
 
0.3%
0.06 2495
 
0.1%
0.07 2961
 
0.1%
0.08 5317
 
0.2%
0.09 2366
 
0.1%
ValueCountFrequency (%)
498.07 4
< 0.1%
491.51 8
< 0.1%
490 1
 
< 0.1%
488.48 1
 
< 0.1%
487.79 1
 
< 0.1%
487.6 1
 
< 0.1%
487.21 1
 
< 0.1%
487.2 1
 
< 0.1%
486.58 1
 
< 0.1%
485.69 1
 
< 0.1%

Toluene
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct23291
Distinct (%)1.7%
Missing929412
Missing (%)40.9%
Infinite0
Infinite (%)0.0%
Mean15.851404
Minimum0
Maximum499.8
Zeros262296
Zeros (%)11.6%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:42.199394image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.46
median3.67
Q316.78
95-th percentile67.98
Maximum499.8
Range499.8
Interquartile range (IQR)16.32

Descriptive statistics

Standard deviation34.508012
Coefficient of variation (CV)2.1769688
Kurtosis47.468857
Mean15.851404
Median Absolute Deviation (MAD)3.67
Skewness5.6872433
Sum21254165
Variance1190.8029
MonotonicityNot monotonic
2024-12-29T05:29:42.555563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 262296
 
11.6%
0.1 6290
 
0.3%
0.2 5803
 
0.3%
1.1 4880
 
0.2%
0.3 4865
 
0.2%
0.5 4650
 
0.2%
0.6 4032
 
0.2%
0.8 3633
 
0.2%
0.9 3528
 
0.2%
0.4 3351
 
0.1%
Other values (23281) 1037510
45.7%
(Missing) 929412
40.9%
ValueCountFrequency (%)
0 262296
11.6%
0.01 905
 
< 0.1%
0.02 1184
 
0.1%
0.03 1847
 
0.1%
0.04 1001
 
< 0.1%
0.05 1665
 
0.1%
0.06 810
 
< 0.1%
0.07 908
 
< 0.1%
0.08 1635
 
0.1%
0.09 839
 
< 0.1%
ValueCountFrequency (%)
499.8 1
< 0.1%
499.5 2
< 0.1%
499.4 1
< 0.1%
499.2 1
< 0.1%
499.08 1
< 0.1%
499.05 1
< 0.1%
498.9 1
< 0.1%
498.8 1
< 0.1%
498.6 1
< 0.1%
498.2 1
< 0.1%

Xylene
Real number (ℝ)

High correlation  Missing  Zeros 

Distinct5812
Distinct (%)1.2%
Missing1781769
Missing (%)78.5%
Infinite0
Infinite (%)0.0%
Mean2.4422641
Minimum0
Maximum499.99
Zeros207980
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:42.869696image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.15
Q31.65
95-th percentile11.02
Maximum499.99
Range499.99
Interquartile range (IQR)1.65

Descriptive statistics

Standard deviation9.1703914
Coefficient of variation (CV)3.7548729
Kurtosis428.41132
Mean2.4422641
Median Absolute Deviation (MAD)0.15
Skewness16.100857
Sum1192999.6
Variance84.096079
MonotonicityNot monotonic
2024-12-29T05:29:43.216108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 207980
 
9.2%
0.1 11393
 
0.5%
2 6134
 
0.3%
0.65 5562
 
0.2%
0.2 3928
 
0.2%
0.03 3694
 
0.2%
0.3 3685
 
0.2%
0.05 3571
 
0.2%
0.4 3362
 
0.1%
0.08 2910
 
0.1%
Other values (5802) 236262
 
10.4%
(Missing) 1781769
78.5%
ValueCountFrequency (%)
0 207980
9.2%
0.01 1917
 
0.1%
0.02 1525
 
0.1%
0.03 3694
 
0.2%
0.04 1756
 
0.1%
0.05 3571
 
0.2%
0.06 1121
 
< 0.1%
0.07 1476
 
0.1%
0.08 2910
 
0.1%
0.09 973
 
< 0.1%
ValueCountFrequency (%)
499.99 1
< 0.1%
476.31 1
< 0.1%
461.39 1
< 0.1%
433.94 1
< 0.1%
423.48 1
< 0.1%
422.86 1
< 0.1%
419.88 1
< 0.1%
406.29 1
< 0.1%
402.64 1
< 0.1%
398.9 1
< 0.1%

AQI
Real number (ℝ)

High correlation  Missing 

Distinct1601
Distinct (%)0.1%
Missing509518
Missing (%)22.4%
Infinite0
Infinite (%)0.0%
Mean182.43495
Minimum5
Maximum3133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 MiB
2024-12-29T05:29:43.539422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile45
Q187
median133
Q3260
95-th percentile422
Maximum3133
Range3128
Interquartile range (IQR)173

Descriptive statistics

Standard deviation143.06418
Coefficient of variation (CV)0.78419282
Kurtosis40.535023
Mean182.43495
Median Absolute Deviation (MAD)63
Skewness3.6413137
Sum3.2121905 × 108
Variance20467.359
MonotonicityNot monotonic
2024-12-29T05:29:43.870260image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
102 14853
 
0.7%
101 13973
 
0.6%
104 13829
 
0.6%
100 12820
 
0.6%
106 12629
 
0.6%
103 12561
 
0.6%
105 11958
 
0.5%
108 11437
 
0.5%
110 11211
 
0.5%
109 11073
 
0.5%
Other values (1591) 1634388
72.0%
(Missing) 509518
 
22.4%
ValueCountFrequency (%)
5 4
 
< 0.1%
6 34
 
< 0.1%
7 64
< 0.1%
8 27
 
< 0.1%
9 62
< 0.1%
10 112
< 0.1%
11 102
< 0.1%
12 126
< 0.1%
13 127
< 0.1%
14 152
< 0.1%
ValueCountFrequency (%)
3133 8
< 0.1%
3111 8
< 0.1%
3084 8
< 0.1%
3057 8
< 0.1%
3043 8
< 0.1%
3001 8
< 0.1%
3000 8
< 0.1%
2996 8
< 0.1%
2987 8
< 0.1%
2969 8
< 0.1%

AQI_Bucket
Categorical

Missing 

Distinct6
Distinct (%)< 0.1%
Missing509518
Missing (%)22.4%
Memory size136.8 MiB
Moderate
612335 
Satisfactory
444915 
Very Poor
257380 
Poor
211506 
Good
124482 

Length

Max length12
Median length9
Mean length8.2685588
Min length4

Characters and Unicode

Total characters14558716
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowModerate
2nd rowModerate
3rd rowModerate
4th rowModerate
5th rowModerate

Common Values

ValueCountFrequency (%)
Moderate 612335
27.0%
Satisfactory 444915
19.6%
Very Poor 257380
11.3%
Poor 211506
 
9.3%
Good 124482
 
5.5%
Severe 110114
 
4.9%
(Missing) 509518
22.4%

Length

2024-12-29T05:29:44.214922image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-29T05:29:44.530553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
moderate 612335
30.3%
poor 468886
23.2%
satisfactory 444915
22.0%
very 257380
12.8%
good 124482
 
6.2%
severe 110114
 
5.5%

Most occurring characters

ValueCountFrequency (%)
o 2243986
15.4%
r 1893630
13.0%
e 1812392
12.4%
a 1502165
10.3%
t 1502165
10.3%
d 736817
 
5.1%
y 702295
 
4.8%
M 612335
 
4.2%
S 555029
 
3.8%
P 468886
 
3.2%
Other values (8) 2529016
17.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14558716
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 2243986
15.4%
r 1893630
13.0%
e 1812392
12.4%
a 1502165
10.3%
t 1502165
10.3%
d 736817
 
5.1%
y 702295
 
4.8%
M 612335
 
4.2%
S 555029
 
3.8%
P 468886
 
3.2%
Other values (8) 2529016
17.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14558716
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 2243986
15.4%
r 1893630
13.0%
e 1812392
12.4%
a 1502165
10.3%
t 1502165
10.3%
d 736817
 
5.1%
y 702295
 
4.8%
M 612335
 
4.2%
S 555029
 
3.8%
P 468886
 
3.2%
Other values (8) 2529016
17.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14558716
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 2243986
15.4%
r 1893630
13.0%
e 1812392
12.4%
a 1502165
10.3%
t 1502165
10.3%
d 736817
 
5.1%
y 702295
 
4.8%
M 612335
 
4.2%
S 555029
 
3.8%
P 468886
 
3.2%
Other values (8) 2529016
17.4%

Interactions

2024-12-29T05:29:08.744906image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:27:56.222171image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:01.899051image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:07.394083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:14.619409image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:20.587561image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:27.819802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:33.024732image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:40.861397image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:46.968485image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:52.948573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:59.385156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:04.236055image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:09.365672image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:27:56.814335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:02.330634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:07.922002image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:15.057829image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:21.026506image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:28.207581image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:34.407570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:41.516271image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:47.416100image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:53.438034image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:59.745533image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:04.535749image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:10.008936image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:27:57.276108image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:02.740819image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:08.617057image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:15.563553image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:21.507206image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:28.612709image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:34.860251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:42.015043image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:47.867704image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:54.038083image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:00.131079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:04.828104image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:10.644178image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:27:57.725457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:03.187144image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:09.284838image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:16.045848image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:22.090278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:29.016527image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:35.334471image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:42.497036image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:48.338753image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:54.574026image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:00.524169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:05.106933image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:11.351175image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:27:58.188738image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:03.610239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:09.961338image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:16.546384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:22.731446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:29.440771image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:35.797417image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:42.969866image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:48.778090image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:55.239729image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:00.903330image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:05.389250image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:11.872169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:27:58.577802image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:04.001215image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:10.581461image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:16.981317image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:23.328600image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:29.845020image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:36.228959image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:43.410574image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:49.189563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:55.746056image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:01.242697image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:05.660629image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:12.327223image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:27:59.042833image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:04.440167image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:11.301971image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:17.472515image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:23.978580image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:30.280873image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:36.734818image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:43.878110image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:49.631236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:56.325032image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:01.650312image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:05.944168image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:12.774238image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:27:59.458660image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:04.822739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:11.926612image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:17.928864image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:24.588081image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:30.697606image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:37.372116image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:44.352010image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:50.070575image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:56.897765image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:02.006517image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:06.231239image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:13.278091image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:27:59.921246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:05.257583image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:12.430876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:18.421040image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:25.288086image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:31.117372image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:37.901532image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:44.815645image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:50.548350image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:57.358647image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:02.420169image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:06.542188image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:13.707371image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:00.335932image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:05.637671image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:12.867857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:18.861752image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:25.924236image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:31.504202image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:38.520384image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:45.262377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:50.974915image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:57.783792image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:02.832389image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:06.887021image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:14.107495image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:00.723024image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:06.002395image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:13.304570image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:19.274503image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:26.587261image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:31.868624image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:39.066085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:45.683610image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:51.393572image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:58.191446image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:03.250812image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:07.337095image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:14.392113image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:01.010211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:06.296751image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:13.637555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:19.607246image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:26.929017image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:32.151563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:39.561748image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:46.013377image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:51.737111image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:58.528154image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:03.543722image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:07.708849image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:14.838342image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:01.466288image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:06.726429image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:14.115923image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:20.103563image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:27.410603image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:32.554839image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:40.177039image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:46.478378image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:52.371251image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:28:58.941876image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:03.934608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-12-29T05:29:08.082664image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-12-29T05:29:44.767349image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AQIAQI_BucketBenzeneCONH3NONO2NOxO3PM10PM2.5SO2TolueneXylene
AQI1.0000.4140.3660.4430.5000.3570.4460.4400.0680.7980.7720.3520.3530.132
AQI_Bucket0.4141.0000.0210.0730.1690.1460.1720.1760.1290.4220.3710.1220.1140.021
Benzene0.3660.0211.0000.3700.3100.2840.4420.393-0.0680.3790.3800.2510.8270.785
CO0.4430.0730.3701.0000.3640.4230.3960.491-0.1490.4510.4290.2710.4150.341
NH30.5000.1690.3100.3641.0000.2600.4380.371-0.0030.4660.4780.2500.3680.019
NO0.3570.1460.2840.4230.2601.0000.4720.763-0.3420.3850.3960.2070.2460.174
NO20.4460.1720.4420.3960.4380.4721.0000.774-0.0720.4820.4680.2670.4570.245
NOx0.4400.1760.3930.4910.3710.7630.7741.000-0.2240.4690.4530.2360.3660.259
O30.0680.129-0.068-0.149-0.003-0.342-0.072-0.2241.000-0.048-0.0490.063-0.084-0.013
PM100.7980.4220.3790.4510.4660.3850.4820.469-0.0481.0000.8700.3320.3440.065
PM2.50.7720.3710.3800.4290.4780.3960.4680.453-0.0490.8701.0000.3040.3350.150
SO20.3520.1220.2510.2710.2500.2070.2670.2360.0630.3320.3041.0000.3600.258
Toluene0.3530.1140.8270.4150.3680.2460.4570.366-0.0840.3440.3350.3601.0000.724
Xylene0.1320.0210.7850.3410.0190.1740.2450.259-0.0130.0650.1500.2580.7241.000

Missing values

2024-12-29T05:29:15.591285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-29T05:29:18.773534image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-29T05:29:28.257257image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

StationIdDatetimePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
0AP0012017-11-24 17:00:0060.5098.002.3530.8018.258.500.111.85126.400.106.100.10NaNNaN
1AP0012017-11-24 18:00:0065.50111.252.7024.2015.079.770.113.17117.120.106.250.15NaNNaN
2AP0012017-11-24 19:00:0080.00132.002.1025.1815.1512.020.112.0898.980.205.980.18NaNNaN
3AP0012017-11-24 20:00:0081.50133.251.9516.2510.2311.580.110.47112.200.206.720.10NaNNaN
4AP0012017-11-24 21:00:0075.25116.001.4317.4810.4312.030.19.12106.350.205.750.08NaNNaN
5AP0012017-11-24 22:00:0069.25108.250.7018.4710.3813.800.19.2591.100.205.020.00NaNNaN
6AP0012017-11-24 23:00:0067.50111.501.0512.157.3017.650.19.40112.700.205.600.10NaNNaN
7AP0012017-11-25 00:00:0068.00111.001.2514.128.5020.280.18.90116.120.205.550.05NaNNaN
8AP0012017-11-25 01:00:0073.00102.000.3014.307.9011.500.311.80121.500.206.600.00NaNNaN
9AP0012017-11-25 02:00:0081.00123.000.8024.8513.8810.280.111.6283.800.236.770.10NaNNaN
StationIdDatetimePM2.5PM10NONO2NOxNH3COSO2O3BenzeneTolueneXyleneAQIAQI_Bucket
2270240TN0032015-12-29 10:00:0028.33NaN4.625.968.62165.342.313.42NaNNaNNaNNaN311.0Very Poor
2270241TN0032015-12-29 11:00:00500.62NaN5.199.9411.0370.242.943.11NaNNaNNaNNaN311.0Very Poor
2270242TN0032015-12-29 12:00:0081.02NaN5.2210.4611.29153.440.683.28NaNNaNNaNNaN311.0Very Poor
2270243TN0032015-12-29 13:00:0032.47NaN5.5210.1411.38142.180.122.81NaNNaNNaNNaN121.0Moderate
2270244TN0032015-12-29 14:00:0038.03NaN4.767.539.5074.520.443.40NaNNaNNaNNaN118.0Moderate
2270245TN0032015-12-29 15:00:0039.05NaN4.897.599.6383.6717.102.79NaNNaNNaNNaN301.0Very Poor
2270246TN0032015-12-29 16:00:0039.98NaN5.017.509.5957.0210.226.40NaNNaNNaNNaN301.0Very Poor
2270247TN0032015-12-29 17:00:0049.09NaN5.309.6410.96117.80NaN3.23NaNNaNNaNNaN301.0Very Poor
2270248TN0032015-12-29 18:00:0043.32NaN5.448.7010.61266.67NaN3.20NaNNaNNaNNaN301.0Very Poor
2270249TN0032015-12-29 19:00:0040.92NaN5.10NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN